Code
# Following https://oliviergimenez.github.io/bayesian-cr-workshop/worksheets/4_demo.html
#
load('./models/cmrNN_OB/tt/runsOut/tt_NN_OB_mostRecent.RData')
out_NN_OB <- d
#Input data
eh <- tar_read(eh_OB_2002_2014_target)19 River 4 (IS) - CJS model
Simple CJS models to get phi and p estimates to compare with Neural Network CMR
In all the models below, 1 = not observed and 2 = observed in the input encounter data.
Encounter data are available here in the eh.csv file.
19.1 [t,t] model
Model with phi and p for each age-in-samples (column in the encounter history file)
Probability of survival (phi) model structure:
phi[t] <- betaInt[t]
Probability of capture (p) model structure:
p[t] <- betaP[t]
Model code is in ./models/cmrNN_OB/tt/modelCMR_tt_NN_OB_functionsToSource.R
Model is run ‘by hand’ in ./models/cmrNN_OB/tt/modelCMR_tt_NN_OB_makeFile.R
Not using this: Targets are loaded in R/modelCMR_tt_NN_OB.R and saved as modelCMR_tt_NN_OB_target
Model runs:
tt_NN_OB_2023-06-14 23.RData: preliminary run only tracing phi and p
tt_NN_OB_2023-06-15 08.RData: tracing phi and p and z [most recent]
19.1.1 Retrieve model results
19.1.2 Model code
In the model code, a value of 1 for z or in gamma or omega signifies the individual is alive and a value of 2 signifies the individual is dead. y[i,j] is the observed data (encounter history file).
Code
out_NN_OB$modelCode{
delta[1] <- 1
delta[2] <- 0
for (t in 1:(T - 1)) {
phi[t] ~ dunif(0, 1)
gamma[1, 1, t] <- phi[t]
gamma[1, 2, t] <- 1 - phi[t]
gamma[2, 1, t] <- 0
gamma[2, 2, t] <- 1
p[t] ~ dunif(0, 1)
omega[1, 1, t] <- 1 - p[t]
omega[1, 2, t] <- p[t]
omega[2, 1, t] <- 1
omega[2, 2, t] <- 0
}
for (i in 1:N) {
z[i, first[i]] ~ dcat(delta[1:2])
for (j in first[i]:(last[i] - 1)) {
z[i, j + 1] ~ dcat(gamma[z[i, j], 1:2, j])
y[i, j + 1] ~ dcat(omega[z[i, j + 1], 1:2, j])
}
}
}19.1.3 Model statistics
| nIter | nBurnin | nChains | thinRate |
|---|---|---|---|
| 5000 | 2000 | 2 | 5 |
[1] “Run time = 33.374 mins”
19.1.4 Plot traces
Code
priors <- runif(out_NN_OB$runData$nIter * out_NN_OB$runData$nChains, 0, 1)
MCMCtrace(object = out_NN_OB$mcmc,
#ISB = FALSE,
#exact = TRUE,
params = c("phi", "p"),
pdf = FALSE,
priors = priors)
19.1.5 Summary data
Code
MCMCplot(object = out_NN_OB$mcmc, params = c("phi"))
Code
MCMCplot(object = out_NN_OB$mcmc, params = c("p"))
Summary values
Code
#|
(summary_tt <- MCMCsummary(object = out_NN_OB$mcmc, params = c("phi", "p"), round = 3) %>%
rownames_to_column(var = "var")) var mean sd 2.5% 50% 97.5% Rhat n.eff
1 phi[1] 0.812 0.022 0.769 0.812 0.854 1.01 290
2 phi[2] 0.810 0.025 0.762 0.811 0.859 1.00 218
3 phi[3] 0.749 0.021 0.708 0.749 0.792 1.01 230
4 phi[4] 0.647 0.021 0.606 0.647 0.689 1.00 361
5 phi[5] 0.640 0.026 0.587 0.640 0.687 1.00 258
6 phi[6] 0.691 0.035 0.629 0.690 0.764 1.05 206
7 phi[7] 0.830 0.051 0.736 0.827 0.932 1.02 100
8 phi[8] 0.330 0.032 0.271 0.330 0.393 1.00 312
9 phi[9] 0.669 0.079 0.528 0.665 0.837 1.01 200
10 phi[10] 0.560 0.090 0.401 0.556 0.754 1.08 188
11 phi[11] 0.684 0.151 0.412 0.679 0.958 1.00 103
12 p[1] 0.597 0.022 0.555 0.597 0.638 1.00 449
13 p[2] 0.448 0.019 0.410 0.447 0.487 1.01 512
14 p[3] 0.707 0.020 0.669 0.707 0.746 1.00 441
15 p[4] 0.659 0.022 0.615 0.659 0.701 1.01 452
16 p[5] 0.634 0.025 0.587 0.634 0.682 1.01 457
17 p[6] 0.499 0.028 0.447 0.500 0.554 1.01 380
18 p[7] 0.701 0.041 0.616 0.701 0.780 1.00 156
19 p[8] 0.783 0.048 0.682 0.788 0.869 1.00 401
20 p[9] 0.605 0.072 0.458 0.608 0.736 1.03 325
21 p[10] 0.692 0.088 0.512 0.695 0.849 1.03 312
22 p[11] 0.733 0.155 0.438 0.727 0.989 1.00 123Code
# To get the mMCMCSummaryRMNA funcion which I adapted to deal with NAs
source('./models/cmrNN_OB/tt/modelCMR_tt_NN_OB_functionsToSource.R')
summary_tt_z0 <- MCMCSummaryRMNA(object = out_NN_OB$mcmc, params = c("z"), round = 3) %>%
rownames_to_column(var = "var") |>
mutate(
ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
t0 = str_match(var, "([0-9]+), ([0-9]+)")[,3],
ind = as.numeric(ind0),
t = as.numeric(t0)
) |>
dplyr::select(-c(t0, ind0))Add other variables to summary values
Code
ehLong <-
eh$eh |>
as.data.frame() |>
rownames_to_column("ind0") |>
pivot_longer(starts_with("ais")) |>
mutate(
t = as.numeric(str_match(name, "([0-9]+)")[,1]),
enc = value,
ind = as.numeric(ind0)
) |>
dplyr::select(-c(name, value, ind0))
firstLong <- eh$first |>
as_tibble() |>
rownames_to_column("ind") |>
mutate(ind = as.numeric(ind)) |>
rename(first = value)
lastLong <- eh$last |>
as_tibble() |>
rownames_to_column("ind") |>
mutate(ind = as.numeric(ind)) |>
rename(last = value)
cohortLong <- eh$cohort |>
as_tibble() |>
rownames_to_column("ind") |>
mutate(ind = as.numeric(ind))
summary_tt_z <- summary_tt_z0 |>
left_join(ehLong) |>
mutate(
meanM1 = mean - 1,
pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
enc8 = ifelse(enc == 1, 0.8, 0)
) |>
left_join(firstLong) |>
left_join(lastLong) |>
left_join(cohortLong)Code
ojs_define(numTags_tt_OJS = dim(eh$tags)[1])
ojs_define(summary_tt_z_OJS = (summary_tt_z))19.1.6 Plot the probability of survival (z) for selected individuals
Select one or more individuals :
Code
viewof selectInd_tt = Inputs.select([...Array(numTags_tt_OJS).keys()], {
label: "Which fish?",
value: 1,
step: 1,
multiple: true
})
summary_tt_z_OJS_T = transpose(summary_tt_z_OJS)
summary_tt_z_OJS_T_selected = summary_tt_z_OJS_T.filter((d) =>
selectInd_tt.includes(d.ind)
)Black dots/line is estimated probability of survival and orange dots are encountered yes (y = 0.8)/no (y = 0). The green line is the first observation and the red line is the last possible observation. The number in the upper right corner of each panel is the fish’s cohort.
Code
Plot.plot({
width: width,
height: 350,
inset: 10,
color: {
scheme: "greys"
},
x: { label: "Age/season combination" },
y: { label: "Probability of survival" },
marks: [
Plot.frame(),
Plot.dot(summary_tt_z_OJS_T_selected, {
x: "t",
y: "pSurv"
}),
Plot.dot(summary_tt_z_OJS_T_selected, {
x: "t",
y: "enc8",
fill: "orange"
}),
Plot.line(summary_tt_z_OJS_T_selected, {
x: "t",
y: "pSurv"
}),
Plot.ruleX(summary_tt_z_OJS_T_selected, {
x: "first",
y: 1,
stroke: "green"
}),
Plot.ruleX(summary_tt_z_OJS_T_selected, {
x: "last",
y: 1,
stroke: "red"
})
,
Plot.text(summary_tt_z_OJS_T_selected,
Plot.selectFirst({
x: 11,
y: 1,
text: d => d.cohort
})
)
],
facet: {
data: summary_tt_z_OJS_T_selected,
x: "ind"
}
})19.1.7 Output model summary data for Xiaowei
Code
write.csv(summary_tt, './models/cmrNN_OB/tt/dataOut/xiaowei/summary_tt.csv')
write.csv(summary_tt_z, './models/cmrNN_OB/tt/dataOut/xiaowei/summary_tt_z.csv')
# #Output input data to Xiaowei directory. Turn to TRUE to output.
# Output happens here: C:/Users/bletcher/OneDrive - DOI/projects/wbBook_quarto_targets/modelsCMR_NN_OB_outputData.qmd
# if(FALSE) {
#
# for (i in seq_along(eh)){
# write.csv(eh[[i]], file = paste0('./models/cmrNN_OB/tt/dataOut/xiaowei/',
# names(eh)[i],
# ".csv"),
# row.names = F)
# }
# }19.2 [s,s] model
Model with phi and p for each season_isYOY (YOY = young-of-year, isYOY = juvenile or adult) combination. Season is hierarchical over time. There are no data for the isYOY = TRUE, season = 2 state.
Probability of survival (phi) model structure:
phi[yoy, s] <- betaInt[yoy, s]
Probability of capture (p) model structure:
p[yoy, s] <- betaP[yoy, s]
Model code is in ./models/cmrNN_OB/ss/modelCMR_ss_NN_OB_functionsToSource.R
Model is run ‘by hand’ in ./models/cmrNN_OB/ss/modelCMR_ss_NN_OB_makeFile.R
Model runs:
19.2.1 Retrieve Bayesian model results
Code
# Following https://oliviergimenez.github.io/bayesian-cr-workshop/worksheets/4_demo.html
#
load('./models/cmrNN_OB/ss/runsOut/ss_NN_OB_mostRecent.RData')
out_ss_NN_OB <- d
#Input data
eh <- tar_read(eh_OB_2002_2014_target)19.2.2 Model code
In the model code, a value of 1 for z or in gamma or omega signifies the individual is alive and a value of 2 signifies the individual is dead. y[i,j] is the observed data (encounter history file). isYOY = 1 signifies YOY = TRUE (juvenile), and isYOY = 2 signifies YOY = FALSE (adult)
Code
out_ss_NN_OB$modelCode{
{
delta[1] <- 1
delta[2] <- 0
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
gamma[1, 1, t, i] <- phi[i, t]
gamma[1, 2, t, i] <- 1 - phi[i, t]
gamma[2, 1, t, i] <- 0
gamma[2, 2, t, i] <- 1
omega[1, 1, t, i] <- 1 - p[i, t]
omega[1, 2, t, i] <- p[i, t]
omega[2, 1, t, i] <- 1
omega[2, 2, t, i] <- 0
}
}
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
logit(phi[i, t]) <- phiInt[isYOY_DATA[i, t],
seasons[t]]
logit(p[i, t]) <- pInt[isYOY_DATA[i, t], seasons[t]]
}
}
for (yoy in 1:2) {
for (s in 1:4) {
phiInt[yoy, s] ~ T(dnorm(0, sd = 1/0.667), -3.5,
3.5)
pInt[yoy, s] ~ dunif(0, 1)
}
}
for (i in 1:N) {
z[i, first[i]] ~ dcat(delta[1:2])
for (j in first[i]:(last[i] - 1)) {
z[i, j + 1] ~ dcat(gamma[z[i, j], 1:2, j, i])
yDATA[i, j + 1] ~ dcat(omega[z[i, j + 1], 1:2,
j, i])
}
}
}
}19.2.3 Model statistics
| nIter | nBurnin | nChains | thinRate |
|---|---|---|---|
| 5000 | 2000 | 2 | 5 |
[1] “Run time = 38.594 mins”
19.2.4 Plot traces
Code
priors <- runif(out_ss_NN_OB$runData$nIter * out_ss_NN_OB$runData$nChains, 0, 1)
MCMCtrace(object = out_ss_NN_OB$mcmc,
#ISB = FALSE,
#exact = TRUE,
params = c("phiInt", "pInt"),
pdf = FALSE,
priors = priors)
19.2.5 Summary data
Code
#|
MCMCplot(object = out_ss_NN_OB$mcmc, params = c("phiInt"))
Code
MCMCplot(object = out_ss_NN_OB$mcmc, params = c("pInt"))
Summary values
Code
(summary_ss <- tar_read(summary_ss_target)) var mean sd 2.5% 50% 97.5% Rhat n.eff mean_01
1 phiInt[1, 1] 1.356 0.134 1.111 1.347 1.660 1.00 298 0.7951088
2 phiInt[2, 1] 1.204 0.197 0.851 1.191 1.645 1.01 282 0.7692356
3 phiInt[1, 2] -0.008 1.409 -2.749 0.006 2.660 1.00 1200 0.4980000
4 phiInt[2, 2] 0.299 0.073 0.162 0.297 0.441 1.05 327 0.5741980
5 phiInt[1, 3] 1.458 0.127 1.218 1.454 1.713 1.01 368 0.8112266
6 phiInt[2, 3] 0.586 0.098 0.412 0.583 0.795 1.05 363 0.6424468
7 phiInt[1, 4] 1.291 0.116 1.067 1.288 1.507 1.04 424 0.7843164
8 phiInt[2, 4] 0.737 0.133 0.497 0.730 1.005 1.01 298 0.6763395
9 pInt[1, 1] 0.773 0.093 0.585 0.774 0.958 1.02 402 0.6841695
10 pInt[2, 1] 0.929 0.060 0.785 0.945 0.997 1.04 591 0.7168724
11 pInt[1, 2] 0.498 0.290 0.031 0.484 0.974 1.00 1200 0.6219892
12 pInt[2, 2] 0.731 0.088 0.556 0.732 0.904 1.01 549 0.6750247
13 pInt[1, 3] 0.405 0.080 0.254 0.406 0.562 1.00 636 0.5998884
14 pInt[2, 3] 0.548 0.097 0.349 0.552 0.742 1.01 469 0.6336715
15 pInt[1, 4] 0.022 0.019 0.001 0.017 0.073 1.00 489 0.5054998
16 pInt[2, 4] 0.117 0.078 0.006 0.103 0.295 1.01 527 0.5292167Code
summary_ss_z0 <- tar_read(summary_ss_z0_target)
summary_ss_phi0 <- tar_read(summary_ss_phi0_target)
summary_ss_p0 <- tar_read(summary_ss_p0_target)
# (summary_ss <- MCMCsummary(object = out_ss_NN_OB$mcmc, params = c("phiInt", "pInt"), round = 3) %>%
# rownames_to_column(var = "var") |>
# mutate(mean_01 = exp(mean)/(1+exp(mean))))
#
# # To get the mMCMCSummaryRMNA function which I adapted to deal with NAs
# source('./models/cmrNN_OB/ss/modelCMR_ss_NN_OB_functionsToSource.R')
#
# summary_ss_z0 <- MCMCSummaryRMNA(object = out_ss_NN_OB$mcmc, params = c("z"), round = 3) %>%
# rownames_to_column(var = "var") |>
# mutate(
# ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
# t0 = str_match(var, "([0-9]+), ([0-9]+)")[,3],
# ind = as.numeric(ind0),
# t = as.numeric(t0)
# ) |>
# dplyr::select(-c(t0, ind0))
#
# # 'phi' has each individual, 'phiInt' does not
# summary_ss_phi0 <- MCMCSummaryRMNA(object = out_ss_NN_OB$mcmc, params = c("phi"), round = 3) %>%
# rownames_to_column(var = "var") |>
# mutate(
# ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
# t0 = str_match(var, "([0-9]+), ([0-9]+)")[,3],
# ind = as.numeric(ind0),
# t = as.numeric(t0)
# ) |>
# dplyr::select(-c(t0, ind0))
#
# summary_ss_p0 <- MCMCSummaryRMNA(object = out_ss_NN_OB$mcmc, params = c("p"), round = 3) %>%
# rownames_to_column(var = "var") |>
# mutate(
# ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
# t0 = str_match(var, "([0-9]+), ([0-9]+)")[,3],
# ind = as.numeric(ind0),
# t = as.numeric(t0)
# ) |>
# dplyr::select(-c(t0, ind0))Add other variables to summary values
Code
ehLong <- tar_read(ehLong_target)
firstLong <-tar_read(firstLong_target)
lastLong <- tar_read(lastLong_target)
cohortLong <- tar_read(cohortLong_target)
#### z
summary_ss_z <- tar_read(summary_ss_z_target)
##### p
summary_ss_p <- tar_read(summary_ss_p_target)
#### phi
summary_ss_phi <- tar_read(summary_ss_phi_target)
# ehLong <-
# eh$eh |>
# as.data.frame() |>
# rownames_to_column("ind0") |>
# pivot_longer(starts_with("ais")) |>
# mutate(
# t = as.numeric(str_match(name, "([0-9]+)")[,1]),
# enc = value,
# ind = as.numeric(ind0)
# ) |>
# dplyr::select(-c(name, value, ind0))
#
# firstLong <- eh$first |>
# as_tibble() |>
# rownames_to_column("ind") |>
# mutate(ind = as.numeric(ind)) |>
# rename(first = value)
#
# lastLong <- eh$last |>
# as_tibble() |>
# rownames_to_column("ind") |>
# mutate(ind = as.numeric(ind)) |>
# rename(last = value)
#
# cohortLong <- eh$cohort |>
# as_tibble() |>
# rownames_to_column("ind") |>
# mutate(ind = as.numeric(ind))
#
# #### z
# summary_ss_z <- summary_ss_z0 |>
# left_join(ehLong) |>
# mutate(
# meanM1 = mean - 1,
# pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
# enc8 = ifelse(enc == 1, 0.8, 0)
# ) |>
# left_join(firstLong) |>
# left_join(lastLong) |>
# left_join(cohortLong)
#
# ##### p
# summary_ss_p <- summary_ss_p0 |>
# left_join(ehLong) |>
# mutate(
# meanM1 = mean - 1,
# pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
# enc8 = ifelse(enc == 1, 0.2, 0)
# ) |>
# left_join(firstLong) |>
# left_join(lastLong) |>
# left_join(cohortLong)
#
# #### phi
# summary_ss_phi <- summary_ss_phi0 |>
# left_join(ehLong) |>
# mutate(
# meanM1 = mean - 1,
# pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
# enc8 = ifelse(enc == 1, 0.2, 0)
# ) |>
# left_join(firstLong) |>
# left_join(lastLong) |>
# left_join(cohortLong)19.2.6 Retrieve ML model results
ML model result are from Xioawei and Erhu at PITT
Code
if(!exists("np")) {
np <-import("numpy")
}
xP0 <- np$load("./data/in/fromXioawei/p_est.npy")
xP <- xP0 |>
as.data.frame() |>
rowid_to_column("ind") |>
pivot_longer(cols = starts_with("V")) |>
separate_wider_delim(name, delim = "V", names = c("d", "t")) |>
dplyr::select(-d) |>
mutate(t = as.numeric(t)) |>
rename(xP = value)
xPhi0 <- np$load("./data/in/fromXioawei/phi_est.npy")
xPhi <- xPhi0 |>
as.data.frame() |>
rowid_to_column("ind") |>
pivot_longer(cols = starts_with("V")) |>
separate_wider_delim(name, delim = "V", names = c("d", "t")) |>
dplyr::select(-d) |>
mutate(t = as.numeric(t))|>
rename(xPhi = value)Plot ML model results
Code
ggplot(xP, aes(t, xP)) +
geom_point(alpha = 0.01)
Code
ggplot(xPhi, aes(t, xPhi)) +
geom_point(alpha = 0.01)
Plot ML model results - jittered
Code
ggplot(xP, aes(t, xP)) +
geom_jitter(alpha = 0.01)
Code
ggplot(xPhi, aes(t, xPhi)) +
geom_jitter(alpha = 0.01)
Group by individual
Code
ggplot(xP, aes(t, xP, group = ind)) +
geom_line()
Code
ggplot(xPhi, aes(t, xPhi, group = ind)) +
geom_line()
19.2.7 Summarize ML model results
Code
summary_xPhi <- xPhi |>
summarize(
meanPhi = mean(xPhi),
sdPhi = sd(xPhi)
)
summary_xP <- xP |>
summarize(
meanP = mean(xP),
sdP = sd(xP)
)19.2.8 Merge ML model results with Bayesian results
Code
summary_ss_phi_x <- summary_ss_phi |>
left_join(xPhi)
summary_ss_p_x <- summary_ss_p |>
left_join(xP)Plot Bayes and ML model results for individual fish
Code
ggplot(summary_ss_phi_x, aes(mean, xPhi, color = factor(t))) +
geom_jitter(alpha = 0.01)
Code
ggplot(summary_ss_p_x, aes(mean, xP, color = factor(t))) +
geom_jitter(alpha = 0.01)
Observable interactivity
Code
ojs_define(numTags_ss_OJS = dim(eh$tags)[1])
ojs_define(summary_ss_z_OJS = (summary_ss_z))
ojs_define(summary_ss_phi_x_OJS = (summary_ss_phi_x))
ojs_define(summary_ss_p_x_OJS = (summary_ss_p_x))Code
summary_ss_phi_x_OJS19.2.9 Plot the probabilities for selected individuals
Probabilities are: alive (z), survival (phi), and detection (p)
Code
summary_ss_z_OJS_T_selectedCode
summary_ss_phi_x_OJS_T_selectedCode
summary_ss_p_x_OJS_T_selectedCode
viewof selectInd_ss = Inputs.select([...Array(numTags_ss_OJS).keys()], {
label: "Which fish?",
value: 1,
step: 1,
multiple: true
})
summary_ss_z_OJS_T = transpose(summary_ss_z_OJS)
summary_ss_z_OJS_T_selected = summary_ss_z_OJS_T.filter((d) =>
selectInd_ss.includes(d.ind)
)
summary_ss_phi_x_OJS_T = transpose(summary_ss_phi_x_OJS)
summary_ss_phi_x_OJS_T_selected = summary_ss_phi_x_OJS_T.filter((d) =>
selectInd_ss.includes(d.ind)
)
summary_ss_p_x_OJS_T = transpose(summary_ss_p_x_OJS)
summary_ss_p_x_OJS_T_selected = summary_ss_p_x_OJS_T.filter((d) =>
selectInd_ss.includes(d.ind)
)Black dots/line is estimated probability of being alive and orange dots are encountered yes (y = 0.8)/no (y = 0). The green line is the first observation and the red line is the last possible observation. The number in the upper right corner of each panel is the fish’s cohort.
Code
Plot.plot({
width: width,
height: 350,
inset: 10,
color: {
scheme: "greys"
},
x: { label: "Age/season combination" },
y: { label: "Probability of alive" },
marks: [
Plot.frame(),
Plot.dot(summary_ss_z_OJS_T_selected, {
x: "t",
y: "pSurv"
}),
Plot.dot(summary_ss_z_OJS_T_selected, {
x: "t",
y: "enc8",
fill: "orange"
}),
Plot.line(summary_ss_z_OJS_T_selected, {
x: "t",
y: "pSurv"
}),
Plot.ruleX(summary_ss_z_OJS_T_selected, {
x: "first",
y: 1,
stroke: "green"
}),
Plot.ruleX(summary_ss_z_OJS_T_selected, {
x: "last",
y: 1,
stroke: "red"
})
,
Plot.text(summary_ss_z_OJS_T_selected,
Plot.selectFirst({
x: 11,
y: 1,
text: d => d.cohort
})
)
],
facet: {
data: summary_ss_z_OJS_T_selected,
x: "ind"
}
})19.2.10 Compare survival with ML survival estimates
ML estimates are in orange
Code
Plot.plot({
width: width,
height: 350,
inset: 10,
color: {
scheme: "greys"
},
x: { label: "Age/season combination" },
y: { label: "Probability of survival" },
marks: [
Plot.frame(),
Plot.dot(summary_ss_phi_x_OJS_T_selected, {
x: "t",
y: "mean"
}),
Plot.dot(summary_ss_phi_x_OJS_T_selected, {
x: "t",
y: "enc8"
}),
Plot.line(summary_ss_phi_x_OJS_T_selected, {
x: "t",
y: "mean"
}),
Plot.dot(summary_ss_phi_x_OJS_T_selected, {
x: "t",
y: "xPhi",
stroke: "orange"
}),
Plot.line(summary_ss_phi_x_OJS_T_selected, {
x: "t",
y: "xPhi",
stroke: "orange"
}),
Plot.ruleX(summary_ss_phi_x_OJS_T_selected, {
x: "first",
y: 1,
stroke: "green"
}),
Plot.ruleX(summary_ss_phi_x_OJS_T_selected, {
x: "last",
y: 1,
stroke: "red"
})
,
Plot.text(summary_ss_phi_x_OJS_T_selected,
Plot.selectFirst({
x: 11,
y: 1,
text: d => d.cohort
})
)
],
facet: {
data: summary_ss_phi_x_OJS_T_selected,
x: "ind"
}
})19.2.11 Compare probability of capture with ML survival estimates
ML estimates are in orange
Code
Plot.plot({
width: width,
height: 350,
inset: 10,
color: {
scheme: "greys"
},
x: { label: "Age/season combination" },
y: { label: "Probability of capture" },
marks: [
Plot.frame(),
Plot.dot(summary_ss_p_x_OJS_T_selected, {
x: "t",
y: "mean"
}),
Plot.dot(summary_ss_p_x_OJS_T_selected, {
x: "t",
y: "enc8"
}),
Plot.line(summary_ss_p_x_OJS_T_selected, {
x: "t",
y: "mean"
}),
Plot.dot(summary_ss_p_x_OJS_T_selected, {
x: "t",
y: "xP",
stroke: "orange"
}),
Plot.line(summary_ss_p_x_OJS_T_selected, {
x: "t",
y: "xP",
stroke: "orange"
}),
Plot.ruleX(summary_ss_p_x_OJS_T_selected, {
x: "first",
y: 1,
stroke: "green"
}),
Plot.ruleX(summary_ss_p_x_OJS_T_selected, {
x: "last",
y: 1,
stroke: "red"
})
,
Plot.text(summary_ss_p_x_OJS_T_selected,
Plot.selectFirst({
x: 11,
y: 1,
text: d => d.cohort
})
)
],
facet: {
data: summary_ss_p_x_OJS_T_selected,
x: "ind"
}
})19.2.12 Output model summary data for ML model
Code
write.csv(summary_ss, './models/cmrNN_OB/ss/dataOut/xiaowei/summary_ss.csv')
write.csv(summary_ss_z, './models/cmrNN_OB/ss/dataOut/xiaowei/summary_ss_z.csv')
# #Output input data to Xiaowei directory. Turn to TRUE to output.
# Output happens here: C:/Users/bletcher/OneDrive - DOI/projects/wbBook_quarto_targets/modelsCMR_NN_OB_outputData.qmd
# if(FALSE) {
#
# for (i in seq_along(eh)){
# write.csv(eh[[i]], file = paste0('./models/cmrNN_OB/tt/dataOut/xiaowei/',
# names(eh)[i],
# ".csv"),
# row.names = F)
# }
# }